Including Plots
library(tidyverse)
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## ✓ tibble 3.1.0 ✓ dplyr 1.0.5
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(ggthemes)
library(ggrepel)
library(gridExtra)
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## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
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## combine
library(hexbin)
library(ggExtra)
library(cowplot)
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## Attaching package: 'cowplot'
## The following object is masked from 'package:ggthemes':
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## theme_map
library(gtools)
library(knitr)
library(rmarkdown)
#datasets
library(dslabs)
library(nycflights13)
library(NHANES)
library(titanic)
labels_country <-
data.frame(country = c("South Korea","Germany", "India", "China", "Pakistan"),
x = c(1976,1965,1980, 2000, 1970), y = c(68,73,52,68, 52))
gapminder %>%
filter(country %in% c("South Korea","Germany","India","China","Pakistan")) %>%
ggplot(aes(year, life_expectancy, col = country)) + geom_line() +
geom_text(data = labels_country, aes(x, y, label = country), size = 5) +
theme(legend.position = "none") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
gapminder <- gapminder %>%
mutate(dollars_per_day = gdp/population/365)
past_year <- 1970
gapminder %>%
filter(year == past_year & !is.na(gdp)) %>%
mutate(region = reorder(region, dollars_per_day, FUN = median)) %>%
ggplot(aes(region, dollars_per_day)) + geom_boxplot(aes(fill = continent)) +
theme_economist() +theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Region") + ylab("Dollars per day") + scale_fill_discrete("Continents") +
scale_y_continuous(trans = "log2") + geom_point()
view(gapminder)
Males <-
heights %>%
filter(sex == "Male") %>% ggplot(aes(height,height)) +
geom_boxplot() + theme_economist()
Males
## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
Females <-
heights %>%
filter(sex == "Female") %>% ggplot(aes(height,height)) +
geom_boxplot() + theme_economist()
plot_grid(Males, Females, labels = c('Males', 'Females'), label_size = 12)
## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
heights %>%
group_by(sex) %>% ggplot(aes(height)) +
geom_histogram(binwidth = 1, color = "black") +
xlab("Height") + ylab("Count")
heights %>%
ggplot(aes(height)) +
geom_histogram(binwidth = 1, color = "white") +
xlab("Height") + ylab("Count")
str(heights)
## 'data.frame': 1050 obs. of 2 variables:
## $ sex : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 1 1 1 1 2 ...
## $ height: num 75 70 68 74 61 65 66 62 66 67 ...
gender <- data.frame(sex = c("Male", "Female"), x = c(61,75), y = c(0.12, 0.08))
heights %>%
ggplot(aes(height, fill = sex)) + geom_density(alpha = 0.2) +
theme(legend.position = "none") +
geom_text(data = gender, aes(x,y, label = sex), size = 5) +
xlab("Height") + ylab("Density") +
ggtitle(label = "Height across genders", subtitle = "(Density graph)") +
theme(plot.title = element_text(color = "Blue", size = 20,
face = "bold", hjust = 0.5)) +
theme(plot.subtitle = element_text(color = "red", size = 15, hjust = 0.5)) +
labs(caption = "By - Abhilash Roy") +
theme(plot.caption = element_text(color = "black", size = 12))
heights %>%
ggplot(aes(sex, height)) + geom_jitter(width = 0.1, alpha = 0.4) +
labs(x = "Sex", y = "Height", title = "Heights of Genders")
gapminder %>%
filter(year %in% c(1960,1970)) %>% ggplot(aes(fertility,
life_expectancy, color = continent)) + geom_point() + facet_grid(~year) +
theme_economist() + xlab("Fertility") + ylab("Life Expectancy(in years)") +
scale_color_discrete("Continents")
gapminder %>%
filter(year %in% c(1960,1970,1980,1990,2000,2010)) %>%
ggplot(aes(fertility, life_expectancy, color = continent)) + geom_point() +
facet_wrap(~year) + theme_economist() + xlab("Fertility") +
ylab("Life Expectancy(in years)") +
scale_color_discrete("Continents")
data(iris)
iris %>%
ggplot(aes(Sepal.Length, Sepal.Width, color = Species, shape = Species)) +
geom_point() +theme_light() + geom_density2d() + ggtitle("IRIS")
iris %>%
mutate(Species = "All") %>% rbind(iris) %>%
ggplot(aes(Petal.Length, Petal.Width, color = Species, shape =
Species)) + geom_point() + theme_bw() + facet_wrap(~Species, scales =
"free") + geom_smooth() + xlab("Petal Length") +ylab("Petal Width")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
mtcars %>% ggplot(aes(mpg, qsec, color = gear, size = disp)) +
geom_point() + xlab("Miles/(US) gallon") + ylab("1/4 mile time") +
theme_bw() + scale_size_continuous("Displacement")
gapminder %>%
mutate(dollars_per_day = gdp/population/365) %>%
filter(continent == "Africa", year %in%
c(1970,2010), !is.na(dollars_per_day)) %>%
ggplot(aes(dollars_per_day)) + geom_density() + facet_grid(~year) +
scale_x_continuous(trans = "log2")
gapminder %>%
mutate(dollars_per_day = gdp/population/365) %>%
filter(continent == "Africa", year %in% c(1970,2010),
!is.na(dollars_per_day)) %>% ggplot(aes(dollars_per_day, fill = region)) +
geom_density(bw= 0.5,position = "stack") + facet_grid(~year) +
scale_x_continuous(trans = "log2")
data(temp_carbon)
data(greenhouse_gases)
data(historic_co2)
greenhouse_gases %>%
ggplot(aes(year,concentration)) +
geom_line() +
facet_grid(gas~., scales = "free") +
ylab("Concentration (ch4/n2o ppb, co2 ppm)") +
geom_vline(aes(xintercept = 1850)) +
ggtitle("Atmospheric greenhouse gas concentration by year, 0-2000")
str(greenhouse_gases)
## 'data.frame': 300 obs. of 3 variables:
## $ year : num 20 40 60 80 100 120 140 160 180 200 ...
## $ gas : chr "CO2" "CO2" "CO2" "CO2" ...
## $ concentration: num 278 278 277 277 278 ...
greenhouse_gases %>%
ggplot(aes(year,concentration)) +
geom_line() +
facet_grid(gas~., scales = "free") +
ylab("Concentration (ch4/n2o ppb, co2 ppm)") +
geom_hline(aes(yintercept = 200)) +
ggtitle("Atmospheric greenhouse gas concentration by year, 0-2000")
co2_time <- historic_co2 %>%
ggplot(aes(year, co2, col = source)) + geom_line() +
ggtitle("Atmospheric CO2 concentration, -800,000 BC to today") +
ylab("co2 (ppmv)") + xlim(-800000, -775000) +
geom_hline(aes(yintercept = 200)) +
geom_hline(aes(yintercept = 275))
co2_time
## Warning: Removed 683 row(s) containing missing values (geom_path).
co2_time <- historic_co2 %>%
ggplot(aes(year, co2, col = source)) + geom_line() +
ggtitle("Atmospheric CO2 concentration, -800,000 BC to today") +
ylab("co2 (ppmv)") + xlim(-3000, 2018) +
geom_hline(aes(yintercept = 275)) +
geom_hline(aes(yintercept = 400))
co2_time
## Warning: Removed 539 row(s) containing missing values (geom_path).
temp_carbon %>% filter(!is.na(temp_anomaly),!is.na(land_anomaly),
!is.na(ocean_anomaly)) %>%
ggplot(aes(year, carbon_emissions, color = temp_anomaly)) +geom_line() +
ylab("Temperature anomaly (degrees C)") +
ggtitle("Temperature anomaly relative to 20th century mean, 1880-2018")
## Warning: Removed 4 row(s) containing missing values (geom_path).
murders %>%
ggplot(aes(population/10^6, total, color = region, label = abb)) +
geom_point() + xlab("State population per million (2010)") +
ylab("Number of gun murders in state (2010)") +
geom_text_repel(nudge_x = 0.075) +
scale_x_log10() + scale_y_log10() +
ggtitle("US gun murders by state for 2010",
subtitle = "Gun murder data from FBI reports.") +
theme(plot.title = element_text(hjust = 0.5, color = "blue", size = 15)) +
theme(plot.subtitle = element_text(hjust = 0.5, color = "purple")) +
theme(legend.position = "none")
